A computational framework for exploratory data analysis in biomedical imaging

Axel Wismueller

doi:10.1117/12.813894

27 February 2009 A computational framework for exploratory data analysis in biomedical imaging

Axel Wismueller

Proceedings Volume 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging; 726206 (2009) https://doi.org/10.1117/12.813894
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Purpose: To develop, test, and evaluate a novel unsupervised machine learning method for the analysis of multidimensional biomedical imaging data. Methods: The Exploration Machine (XOM) is introduced as a method for computing low-dimensional representations of high-dimensional observations. XOM systematically inverts functional and structural components of topology-preserving mappings. Thus, it can contribute to both structure-preserving visualization and data clustering. We applied XOM to the analysis of microarray imaging data of gene expression profiles in Saccharomyces cerevisiae, and to model-free analysis of functional brain MRI data by unsupervised clustering. For both applications, we performed quantitative comparisons to results obtained by established algorithms. Results: Genome data: Absolute (relative) Sammon error values were 2.21 · 10³ (1.00) for XOM, 2.45 · 10³ (1.11) for Sammon's mapping, 2.77 · 10³ (1.25) for Locally Linear Embedding (LLE), 2.82 · 10³ (1.28) for PCA, 3.36 · 10³ (1.52) for Isomap, and 10.19 · 10³(4.61) for Self-Organizing Map (SOM). - Functional MRI data: Areas under ROC curves for detection of task-related brain activation were 0.984 ± 0.03 for XOM, 0.983 ± 0.02 for Minimal-Free-Energy VQ, and 0.979 ± 0.02 for SOM. Conclusion: We introduce the Exploration Machine as a novel machine learning method for the analysis of multidimensional biomedical imaging data. XOM can be successfully applied to microarray gene expression analysis and to clustering of functional brain MR image time-series. By simultaneously contributing to dimensionality reduction and data clustering, XOM is a useful novel method for data analysis in biomedical imaging.

Citation Download Citation

Axel Wismueller "A computational framework for exploratory data analysis in biomedical imaging", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 726206 (27 February 2009); https://doi.org/10.1117/12.813894

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